import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = AutoPipelineForInpainting.from_pretrained("Liangyingping/Lift3Dreamer", dtype=torch.float16, device_map="cuda")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
image = load_image(img_url).resize((1024, 1024))
mask_image = load_image(mask_url).resize((1024, 1024))
prompt = "a tiger sitting on a park bench"
generator = torch.Generator(device="cuda").manual_seed(0)
image = pipe(
prompt=prompt,
image=image,
mask_image=mask_image,
guidance_scale=8.0,
num_inference_steps=20, # steps between 15 and 30 work well for us
strength=0.99, # make sure to use `strength` below 1.0
generator=generator,
).images[0]YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Usage:
from diffusers import StableDiffusionInpaintPipeline
import torch
from diffusers.utils import load_image, make_image_grid
import PIL
# 指定模型文件路径
model_path = "Liangyingping/Lift3Dreamer"
# 加载模型
pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_path, torch_dtype=torch.float16
)
pipe.to("cuda") # 如果有 GPU，可以将模型加载到 GPU 上
init_image = load_image("assets/debug_masked_image.png")
mask_image = load_image("assets/debug_mask.png")
W, H = init_image.size
prompt = "a photo of a person"
image = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
h=512, w=512
).images[0].resize((W, H))
print(image.size, init_image.size)
image2save = make_image_grid([init_image, mask_image, image], rows=1, cols=3)
image2save.save("image2save_ours.png")
If you use this inpainting model, please cite our paper:
@article{LIANG2026,
title = {Lift3Dreamer: Boosting Text-Driven Novel View Synthesis via Lifted 3D Inpainting Model from Single Images.},
journal = {Fundamental Research},
year = {2026},
issn = {2667-3258},
author = {Yingping Liang and Ying Fu and Jiaming Liu and Debing Zhang},
}
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